The task of machine condition monitoring is to detect faults as early as possible to avoid further damage to a machine. This is usually done by analyzing data from a set of sensors, installed on different parts of a machine, for measuring temperature, pressure, vibrations, etc. When a machine is operating normally, all sensors obey a certain relationship. That relation can be described by the dependency of one sensor against other related sensors. During monitoring, violation of that relationship or dependency may indicate a fault. For example, in a gas turbine, given certain system inputs such as gas flow, inlet temperature and air humidity, the power output should be close to a predicted value. If the actual observed value deviates from that predicted value, the observation may indicate a system failure.
A fundamental step in machine condition monitoring is to build state estimation (SE) models that describe the relation among a set of sensors. During training, the SE model is trained to learn the sensor relationships from historical training data. During testing, for observed sensor values, the trained SE model is used to estimate the values that sensors should have if they operate normally.
One challenge in creating the SE model is that there are usually many sensors. In many circumstances, the relation among sensors is unknown. Sensors may monitor totally independent parts of the machine so that some sensors are not correlated with other sensors. If one simply builds a single SE model using all sensors, and estimates one sensor using the remaining sensors including unrelated sensors, performance of the SE model will be adversely affected.
In one approach, the SE model is constructed in two steps. First, pair-wise correlation scores of sensors are computed. The scores may be computed by standard correlation coefficients for linear cases, or by more sophisticated mutual information for nonlinear cases. In the second step, based on the correlation scores, a clustering method such as hierarchical clustering is applied to cluster sensors into groups. That approach is limited in that only pair-wise correlation between two sensors is used, and the approach thus cannot capture correlation involving more than two sensors, which exists extensively in complex machines.
Mutual information can be extended for multiple sensors, but that is at the cost of an exponential increase in computation time. In addition, mutual information usually requires discretization of continuous sensor signals, leading to a loss of precision.
There is therefore presently a need for an improved technique to partition sensors into groups, and to monitor machines using such groups. The technique should create groups wherein, within each group sensors are correlated, but between groups, sensors are not correlated. By using such groups, one SE model can be trained for each group.